Search Results for "optimization fortran"

PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell. PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for...

Airfoil optimization using the highly-regarded Xfoil engine for aerodynamic calculations. Starting with a seed airfoil, Xoptfoil uses particle swarm, genetic algorithm and direct search methodologies to perturb the geometry and maximize performance. The user selects a number of operating points over which to optimize, desired constraints, and the optimizer does the rest.

High level optimization routines in Fortran 95 for optimization problems using a genetic algorithm with elitism, steady-state-reproduction, dynamic operator scoring by merit, no-duplicates-in-population. Chromosome representation may be integer-array, real-array, permutation-array, character-array. Single objective and multi-objective maximization routines are present.

JPOP is a pure java parallel optimization package based on the Optimization Java Package. It supports analytical gradients and Hessians for non-linear optimization. JPOP is based on uncmin in Fortran but employs java arrays and object oriented code.

F2what is an open source project to translate algorithmic fortran to a variety of languages (java, dotnet and VBA so far). It offers a Fortran77 parser with code transformations, variable disambiguation, and modular output to other programming languages. It also offers already translated versions of Lapack and quadprog to Java, .Net and VBA, to give access to SVD, LU, QR, Cholesky, eigenvalue decomposition, linear system solving and QP optimization in those three languages.

A Python environment for large and sparse linear and nonlinear optimization using intuitive interfaces to linear algebra tools and subproblem solvers written in low-level languages. It provides building blocks to experiment with new ideas and algorithms.